The present invention relates to methods and apparatuses for estimating channel quality information (CQI) in a communication system in which CQI pilots are transmitted on a plurality of sub-carriers.
In the forthcoming evolution of the mobile cellular standards like the Global System for Mobile Communication (GSM) and Wideband Code Division Multiple Access (WCDMA), new transmission techniques like Orthogonal Frequency Division Multiplexing (OFDM) are likely to occur. Furthermore, in order to have a smooth migration from the existing cellular systems to the new high capacity high data rate system in existing radio spectrum, a new system has to be able to utilize a bandwidth of varying size. A proposal for such a new flexible cellular system, called Third Generation Long Term Evolution (3G LTE), can be seen as an evolution of the 3G WCDMA standard. This system will use OFDM as the multiple access technique (called OFDMA) in the downlink and will be able to operate on bandwidths ranging from 1.25 MHz to 20 MHz. Furthermore, data rates up to 100 Mb/s will be supported for the largest bandwidth. However, it is expected that 3G LTE will be used not only for high rate services, but also for low rate services like voice. Since 3G LTE is designed for Transmission Control Protocol/Internet Protocol (TCP/IP), Voice over IP (VoIP) will likely be the service that carries speech.
There are several reasons why OFDM has been chosen for the LTE system. One is that receiver complexity can be made relatively low. Another reason is that it, at least in theory, allows for very efficient usage of the available bandwidth. In case only one user is transmitting, it is possible to exploit that the channel quality typically is very different at different frequencies (in this respect, the channel is said to be “frequency selective”). Since the information in OFDM is transmitted on a large number of sub-carriers, different modulation and coding techniques can be applied on different sub-carriers, rather than using the same modulation and coding techniques on all sub-carriers. However, in order for this to be possible, the quality of the different sub-carriers of the channel (for instance the signal-to-noise-ratio) must be estimated and these estimates must be fed back to the transmitter.
In case several users are sharing the available bandwidth, the term orthogonal frequency division multiple access (OFDMA) is often used. In OFDMA, the sharing of the channel is achieved by allocating different sub-carriers to different users. The allocation of the sub-carriers to the different users can vary from one symbol to the next, so the channel is effectively divided in both time and frequency. For a cellular system with one base station and several mobile stations, the channels from the base stations to the different mobile stations vary differently and typically independently of one another. With respect to OFDMA, the idea of transmitting more information on the sub-carriers that have good quality, as described above, can be generalized in the following way. The quality on all sub-carriers for all users is determined. The base station then decides not only what the optimum modulation and coding techniques are, but also which sub-carriers should be allocated to which users.
Although the potential gain to be achieved by using adaptive modulation and coding is considerable, it is not so easily obtained in practice. First, in order to estimate the channel quality, known symbols must be transmitted. Henceforth, these symbols are referred to as channel quality information (CQI) pilots. The cost of transmitting CQI pilots is two-fold. First, part of the transmitted power is used for transmitting pilots rather than actual data. Second, the effective data rate that can be supported is reduced, since some of the symbols are not carrying any user data.
The reason why the potential gain is not obtained is that the channel's effect on the transmitted signal cannot be made perfectly known to the transmitting side. One reason for this is that the channel is time-varying. This means that even if the channel were to be estimated perfectly at the receiver side, the channel will have changed by the time that information became available at the transmitter side.
Another reason why the potential gain is not obtained is that the channel quality is not estimated accurately enough. Since the CQI pilots intended for estimating the channel come at the cost of reduced data throughput, the number of CQI pilots is often very small, which implies that the accuracy of the channel estimate by necessity will be limited. An additional problem is that when CQI pilots are transmitted on different sub-carriers, they are affected differently by, for example, the channel. That the CQI pilots are affected differently is of course not unexpected given that the channel is frequency selective. However, the problem is that in many practical situations the respective phases of two adjacent CQI pilots will change much more than their respective amplitudes.
This means that one cannot estimate the average power by coherently combining the CQI pilots. Instead, one must rely on non-coherent combining. Non-coherent combining is known to have a certain loss compared to coherent combining. Consequently, the estimated channel quality will be less accurate than if coherent combining had been possible, and as a result the system performance will be degraded.
The reason for the quality degradation using non-coherent combining is that non-coherent combining gives rise to a biased estimate as well as an increased variance. In mathematical terms, assuming a channel estimate for a specific sub-carrier is ĥ=h+e, where e is assumed to be complex valued Gaussian noise (variance σ2), and estimating the power (S=|h|2) by coherent averaging over M channel estimates and then using non-coherent averaging over N samples we obtain
                              S          ^                =                              1            N                    ⁢                                    ∑                              l                =                1                            N                        ⁢                                                                                                                        1                      M                                        ⁢                                                                  ∑                                                  k                          =                          1                                                M                                            ⁢                                                                        h                          ^                                                k                                                                                                              l                2                            .                                                          (        1        )            One can now show that
      N          σ      2        ⁢      S    ^  is a sum of non-central χ2N2(λ) distributed random variables, where the non-central parameter λ is
                    λ        =                  2          ⁢                                          ⁢          N          ⁢                                                                                        h                                                  2                                                              σ                  2                                /                M                                      .                                              (        2        )            Applying the central limit theorem, one obtains
                              S          ^                ∈                              N            ⁡                          (                                                                                                                h                                                              2                                    +                                                            σ                      2                                        M                                                  ,                                                                            σ                      2                                                              M                      ⁢                                                                                          ⁢                      N                                                        ⁢                                      (                                                                                            σ                          2                                                M                                            +                                              2                        ⁢                                                                                                          h                                                                                2                                                                                      )                                                              )                                .                                    (        3        )            The derivations of equations (1) through (3) are valid in Additive White Gaussian Noise (AWGN) channels, that is, in which the channel is constant over time and over sub-carriers. In practice, with delay spread (and/or Doppler), the channel is not constant over frequency (and/or time), and hence equation (3) is in that case only an approximation.
From the above it can be seen that                the power estimate, Ŝ, is biased, with a term equal to σ2/M;        the coherent averaging of M channel estimates primarily reduces the bias, which could be a problem for low Signal-to-Interference Ratios (SIRs) if M is small; and        the product NM reduces the variance of the estimate.It can be noted that NM is the total number of pilots available for estimation, and clearly the estimate will have the smallest bias as well as variance if M=NM (i.e., if N=1 meaning that all pilots are coherently combined).        
Estimating the channel quality by using the CQI pilots using non-coherent combining is known. This is a straight-forward approach, but its drawback is that its performance is, in many cases, relatively poor. The reason why non-coherent combining is nonetheless used is simply that no phase knowledge is available in conventional systems, making coherent combining seem infeasible.
A fundamental problem with using pilot symbols that are transmitted on different sub-carriers for CQI estimation is that the phases for the different sub-carriers typically are affected in different and unknown ways from one another. This means that coherent alignment of the pilots before averaging is not feasible.
In order to simplify the description of the invention and to describe why it is not possible, using conventional techniques, to perform coherent combining, but without limiting the scope of the invention in any way, let it be assumed that the parameters for the OFDM system are those currently standardized in 3GPP. Specifically, it will be assumed for the sake of example that the spacing between the sub-carriers is 15 kHz and that the duration of the useful part of the symbol equals the reciprocal of this, that is, tsymb=0.067 ms. For simplicity, suppose that the length of the cyclic prefix (CP) is tCP=4.69 μs. (It is well-known that, in modulation techniques such as OFDM, a transmitted signal comprises a symbol portion and a cyclic prefix that precedes the symbol portion, wherein the cyclic prefix is a replica of a tail portion of the symbol portion.) Moreover, in the 3GPP standard, the total bandwidth is divided into so-called resource blocks, each containing 12 sub-carriers. A resource block constitutes the smallest possible amount of sub-carriers that can be allocated to a user.
Reasons why different sub-carriers are affected differently might be that the channel as such is frequency selective, but it might also be caused by a synchronization error.
First, consider the case in which the channel is frequency selective, and suppose that the channel consists of two taps of equal strength, wherein the delay between the two taps equals Δt seconds. The impulse response of the channel can then, possibly after scaling, be written ash(t)=δ(t)+δ(t−Δt),  (4)and the corresponding channel transfer function is then given byH(f)=1+e−j2πfΔt=2e−jπf Δtcos(πfΔt)  (5)
Now suppose that Δt=2 μs and consider two adjacent sub-carriers. According to equation (5), the phase difference for the channel transfer function for these sub-carriers will be πf ·Δt=0.03π=0.094 rad. In case, for example, the pilots used for CQI estimation are six sub-carriers apart, which is the current assumption in the 3GPP specification, then the phase shift between two pilots will be 0.56 rad, or about 32 degrees. Although this rotation is small enough to allow for coherent combining of two pilots (which corresponds to one resource block), it is clear that using pilots from several resource blocks would result in phase differences that would render coherent combining infeasible.
Now, consider the case in which the channel is frequency flat, but in which ε samples from the CP are used by the Fast Fourier Transform (FFT). (It is well-known that when modulation techniques like OFDM are used, demodulation involves applying a Fourier Transform to the received signal.) FIG. 1, which is a diagram of an exemplary OFDM signal 100 comprising an N-sample wide symbol part 101 and a CP 103, will help illustrate this situation. The CP 103 comprises a copy of information 105 that is also present in a tail portion of the symbol part 101. An initial part, but not all, of the CP 103 also includes inter-symbol interference 107. Suppose an N-sample wide FFT window 109 obtains its first sample at a position 111 that is ε samples earlier than the latest possible position 113 that will still avoid Inter-Symbol Interference (ISI). Note that in this case the start 111 of the FFT window 109 (i.e., the samples used by the FFT) is placed in the middle of the ISI free part of the CP 103. It can be shown that the difference in placing the FFT window as shown in the figure compared to placing it as late as possible (i.e., ε=0) will result in a phase shift at the output of the FFT according toXε(l)=X(l)e−j2πεl/N  (6)where N is the size of the FFT and l is the index of the frequency bin at the output of the FFT and is in the range −N/2+1 to N/2. Now, assuming in our example that the length of the CP is about 7% of N, then a reasonable value of ε/N is, say, 2%. Referring to equation (6), it is readily seen that this produces a phase rotation that changes by 0.12 rad per sub-carrier. Again, if the pilots to be used for CQI estimation are 6 sub-carriers apart, there will be a rotation of 0.72 rad, or equivalently about 41 degrees between the pilots used for CQI estimation. In this example, as in the earlier one, coherent combining over one resource block might be feasible, but coherent combining over several cannot be done using conventional techniques.
Consequently, performance will be degraded whenever several resource blocks are available because one would have to resort to non-coherent combining between the resource blocks. In fact, coherent combining may not even be feasible within a single resource block if a very large delay spread is involved.
In view of the above, there is a need for methods and apparatuses that enable coherent combining of pilots to be performed for CQI estimation, even without any phase reference being available (i.e., without knowing what any actual phase value is for any of the sub-carriers). Such methods and apparatuses would, for example, enhance CQI estimation and by that system performance.